High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons

Abstract Automating dynamic fine root data collection in the field is a longstanding challenge with multiple applications for co-interpretation and synthesis for ecosystem understanding. High frequency root data are only achievable with paired automated sampling and processing. However, automatic minirhizotron (root camera) instruments are still rare and data are often not collected in natural soils or analysed at high temporal resolution. Instruments must also be affordable for replication and robust under variable natural conditions. Here, we show a system built with off-the-shelf parts which samples at sub-daily resolution. We paired this with a neural network to analyse all images collected. We performed two mesocosm studies and two field trials alongside ancillary data collection (soil CO2 efflux, temperature, and moisture content, and ‘PhenoCam’-derived above-ground dynamics). We produce robust and replicated daily time series of root dynamics under all conditions. Temporal root changes were a stronger driver than absolute biomass on soil CO2 efflux in the mesocosm. Proximal sensed above-ground dynamics and below-ground dynamics from minirhizotron data were not synchronized. Root properties extracted were sensitive to soil moisture and occasionally to time of day (potentially relating to soil moisture). This may only affect high frequency imagery and should be considered in interpreting such data.

the computer. A full sampling cycle takes ~ 40 minutes, covering the entire tube and RMR takes 112 removeable 128 GB SD cards; in order to obtain the images, the SD cards were swapped with blank alternatives during periods when the instruments were not powered. A log file was amended at the end of each imaging cycle which could be checked much faster than individual images and easily accessed over WiFi. A single image was less than 1 Mb, therefore the RMR could sample around 1100 cycles without the SD card being changed.
One sampling cycle from the RMR requires about 10 watt hours. In E1 and E2 and E4 the RMR ran on mains power, although on occasion was unplugged for several cycles due to other work in its greenhouse location, and/or minor modifications as in this development phase we still had concerns about cable management in the mechanical part of the device. These modifications did not affect positioning nor later image capture. In E3 the instruments ran on solar power which meant an occasional loss of data in rare extended bad weather when the battery did not recharge sufficiently. The images collected by the RMR overlap by 250 pixels in the lateral direction. For image processing, we trimmed the images to remove overlap so all pixels were independent.
This design of the RMR operates in a 1000 mm long x 100 mm (96 mm internal) diameter observatory. This is comparable to the only other automatic minirhizotron systems used in published experiments (Iversen et al., 2011;Svane et al., 2019).
In the instrument revision between E3 and E4, we made three minor modifications to the RMR. First, we installed a GPS clock which could correct any drift in the BIOS clock due to temperatures (encountered in E3, described in the main manuscript). Second, we added a magnetic sensor to reduce the reliance on mechanical precision to reset the instrument between runs; this prevented a rare but fatal cable management issue if the instrument overshot its 'resting' position. Thirdly, we changed the camera to a IDS 1007XS-C (IDS imaging Development Systems GmBH, Obersulm Germany).
Supplementary Figure S1: Sub-daily data and root diameter artefact With the amount of training we performed (and hence, possibly improvable with more training effort), we encountered an artefact in the sub-daily data. This occurred when there were roots present in the mesocosm, segmented root cover was lower in periods of daylight than darkness (contrast midday and midnight in figure S1).
When we examined the whole segmented dataset, we found that this effect was strongest on the sides of the mesocosm and almost non-existent on the top. We therefore think that this effect was unlikely to be due to 1) sensor degradation, which should not vary within a single sampling routine which passed through a sequence of rotational positions without sampling any one angle completely before any other, 2) light 'leaking' through the shallow soil surface, which should affect the top of the mesocosm more than the bottom nor 3) poor tolerance of the positioning system, where errors should not occur in a structured fashion. Because this effect also only began to occur at levels of higher root biomass, and was difficult to distinguish with the native eye ( Figure S1), we hypothesise that this may be due to changes in the soil appearance linked to diurnal depletion of root zone water linked to transpiration or potentially diurnal variation in root diameter (Huck et al., 1970). Figure S1 -Contrasting sequential day (from image top left) and night segmentations (from image bottom left). Areas in blue are root segmentations in common between a midday and midnight. Red is areas which were identified as roots at night, and soil during the day, while purple is areas that were identified as soil at night and roots during the day. Overall there are also more purple than red segmentations. This effect is common throughout the dataset.
Supplementary Figure S2: Validation against Manual Measurements for Experiment 2, 3, 4. Figure S2: Image-level validation for Experiment 2, 3, and 4. The CNN consistently either over or underestimated the manual sRSA, which could be due to the model training or the validation data R we used.

Supplementary Figure S3: Consistency between mesocosm units
In experiment 2, we were able to segment a similar time series from all 8 mesocosms, with some variation as expected on each individual tube. sRSA was between 3 and 4 % of the images at their maximum and peaked around the same time, shortly after cessation of watering. Universally, root cover declined more slowly and later than GCC ( Figure S5) Figure S3: Comparison of absolute R from all mesocosm units in E2 (mesocosm names M1 through M8). Panel a) shows absolute mean sRSA per mesocosm, absolute difference between mesocosms around 1 % of the total image or 25 % of the total area covered Panel b) shows comparison of GCC and sRSA from all 8 mesocosm units. GCC matched sRSA well in all cases and both indexes peaked after watering finished (blue line in both panels). In most mesocosms GCC reached its maximum before sRSA. Generally, the match between RSA and ingrowth root mass, and root cover and GCC was better at the start than the end of the experiment.